Babelscape/multinerd
Repository for the paper "MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)" (NAACL 2022).
This project provides a massive, pre-annotated dataset of text in 10 languages, sourced from Wikipedia and WikiNews articles. It helps natural language processing (NLP) researchers and data scientists accurately identify and categorize specific entities like people, organizations, locations, diseases, or events within text. The output is structured data with identified entities, their classifications, and links to knowledge bases.
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Use this if you need extensive, fine-grained, multilingual datasets to train or evaluate models for named entity recognition (NER) or entity linking across various languages and content types.
Not ideal if you require perfectly clean, human-annotated data, as this dataset is automatically generated and may contain some errors, especially for less frequent entity categories.
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Jan 30, 2024
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